Table REF  shows the comparison with these methods on CIFAR-10 datasets on ResNet-56 architecture. It shows the comparison of our block pruning and filter and block pruning techniques. It can be seen that one of our methods is second highest in terms of number of flops, third highest in terms of number of parameter reduction but our second method which is a combination of filter and layer pruning, It can be seen that both our models performed worst in maintaining accuracy, this is partly due to difference in baseline accuracy between our experimentation and SOA table [1]}, Our experimentation achieved \(92.75\%\)  accuracy but SOA [1]} achieved \(93.1\%\)  and these results are adjusted to SOA baseline accuracy but our method performed better then SOA in parameters and flops compression. We believe that this drop in accuracy can be reduced by further fine tuning the perimeters.
